That's pretty much the job of test pilots even today. They are usually dual trained pilots and engineers/technicians, and their job is basically to QA new planes. In military aviation especially test pilots are often feeders into the astronaut program (a different but similar type of daredevil).

The Android ecosystem is much more liberal when it comes to RAM because their Dalvik VM JIT (their Java Android Runtime, partially AOT compiled and partially JITed) design is not particularly memory efficient. But the main issue with Android is that their mid/low end (think the Samsung Galaxy A series, the OnePlus Nords, the Motorola Gs etc.) are very inefficient when it comes to single core compute performance compared to iPhones, and it gets worse once you factor in power efficiency. The high end Android flagships running the Snapdragons elites (especially post Oryon acquisition) have no problems matching if not exceeding Apple hardware performance in terms of raw power but they are much more power hungry.

At the end of the day, the current gen of "pocket scale" LLMs are still far from being able to be deployed at scale on mobile. Maybe in another year or two once RAM prices have fallen enough and mobile manufacturers build a lot more matmul and memory circuits into their SoCs instead of a tiny mostly useless "NPU/tensor processor" that doesn't have enough RAM to run anything useful. Current gen production mobile ML still heavily rely on traditional transfer learning like Apple's Core.ML (tiny pre trained models, with perhaps the final layer inference head swapped out).

If you want a good example of production mobile ML, take a look at FUTO keyboard's transformer model for their keystroke prediction/autocorrect which operates in near real-time.

https://github.com/futo-org/android-keyboard/blob/7ebbf5c8b5...

I would say right now 100M-300M params is about the maximum you can do if you want any reasonable level of performance on your "average" non flagship mobile. I would keep to sub 100M for power efficiency sake.

> The students I want to teach are in the same position, and so are the ones in Nigeria and Bangladesh

It does not take $60 to ship small sub $1 chips from Asia to Nigeria/Bangladesh. These two countries are all on global trade routes (both supply and demand) and the expensive last mile delivery costs are fairly low there too.

https://en.wikipedia.org/wiki/Gimli_Glider

There are various theories around model collapse when you train on too much AI generated data (that's not for distillation).

As the old adage goes, US innovates, China imitates, EU regulates.

https://tanstack.com/charts/latest

Are you sure they are using all of their compute on training? Didn't they rent out a ton to other AI companies?

It's just a matter of time before you can post train it for elegance too. Mathematical proofs in particular can be formally verified automatically which is a big advantage.